Visualising Clusters in Self-Organising Maps with Minimum Spanning Trees

Mayer, Rudolf; Rauber, Andreas

doi:10.1007/978-3-642-15822-3_52

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…Hidden features and characteristics of underlying data can be explored, identified and analyzed by utilizing powerful tools of sophisticated visualization techniques. Mayer and Rauber [10] is evaluating novel visualization technique enabled to explore and present structure inherent in the datasets by its application on benchmarked data. Proposed technique is not only able to expose similar data object groups, but also facilitates visualization of similar data objects in graph formation of identical datasets by utilizing minimum spanning trees.…”

Section: Literature Reviewmentioning

confidence: 99%

Clustering Techniques in Bioinformatics

Masood¹,

Khan²,

Zulfikar³

et al. 2015

IJMECS

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Dealing with data means to group information into a set of categories either in order to learn new artifacts or understand new domains. For this purpose researchers have always looked for the hidden patterns in data that can be defined and compared with other known notions based on the similarity or dissimilarity of their attributes according to well-defined rules. Data mining, having the tools of data classification and data clustering, is one of the most powerful techniques to deal with data in such a manner that it can help researchers identify the required information. As a step forward to address this challenge, experts have utilized clustering techniques as a mean of exploring hidden structure and patterns in underlying data. Improved stability, robustness and accuracy of unsupervised data classification in many fields including pattern recognition, machine learning, information retrieval, image analysis and bioinformatics, clustering has proven itself as a reliable tool. To identify the clusters in datasets algorithm are utilized to partition data set into several groups based on the similarity within a group. There is no specific clustering algorithm, but various algorithms are utilized based on domain of data that constitutes a cluster and the level of efficiency required. Clustering techniques are categorized based upon different approaches. This paper is a survey of few clustering techniques out of many in data mining. For the purpose five of the most common clustering techniques out of many have been discussed. The clustering techniques which have been surveyed are: K-medoids, Kmeans, Fuzzy C-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Self-Organizing Map (SOM) clustering.

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Section: Literature Reviewmentioning

confidence: 99%

Clustering Techniques in Bioinformatics

Masood¹,

Khan²,

Zulfikar³

et al. 2015

IJMECS

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show abstract

“…Moving one step forward, we suggested a hybrid procedure that combines together SOM and MST (see also [31]). The idea by itself is not totally newer: [32], for instance, suggested a variant of SOM where neighborhood relationships during the training stage were defined along the MST; [33], and, more recently, [34] applied a MST to SOMs to connect similar nodes with each other, thus visualizing related nodes on the map. In all cited cases this was done by calculating the square difference between neighbor units on the trained map, and using this value to color the edge separating the units.…”

Section: A Hybrid Model Combining Som To Mstmentioning

confidence: 99%

Graph Mining Based SOM: A Tool to Analyze Economic Stability

Resta¹

2012

Applications of Self-Organizing Maps

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“…A simple way to explain this algorithm is to understand the output neurons, represented by the weights ij w computed in the SOM algorithm, as a set of nodes of a fully connected graph (Cormen et al 2001;Pölzlbauer, Rauber, Dittenbach, 2005;Mayer, Rauber, 2010 ) in the parameter space. Each edge in this graph has a cost given by the Euclidean distance between its ends.…”

Section: A Self-organized Manifold Mapping Algorithmmentioning

confidence: 99%